A Rapid, High Throughput, Viral Infectivity Assay using Automated Brightfield Microscopy with Machine Learning

Abstract

AbstractInfectivity assays are essential for the development of viral vaccines, antiviral therapies and the manufacture of biologicals. Traditionally, these assays take 2–7 days and require several manual processing steps after infection. We describe an automated assay (AVIA™), using machine learning (ML) and high-throughput brightfield microscopy on 96 well plates that can quantify infection phenotypes within hours, before they are manually visible, and without sample preparation. ML models were trained on HIV, influenza A virus, coronavirus 229E, vaccinia viruses, poliovirus, and adenoviruses, which together span the four major categories of virus (DNA, RNA, enveloped, and non-enveloped). A sigmoidal function, fit to virus dilution curves, yielded an R2 higher than 0.98 and a linear dynamic range comparable to or better than conventional plaque or TCID50 assays. Because this technology is based on sensitizing AIs to specific phenotypes of infection, it may have potential as a rapid, broad-spectrum tool for virus identification.